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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning.

Yiping Guo1, Guyu Hu1, Dongsheng Shao2

  • 1Command and Control Engineering College, People's Liberation Army Engineering University, Nanjing 210007, China.

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|October 14, 2022
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Summary
This summary is machine-generated.

This study introduces a novel multi-path routing algorithm for wireless sensor networks (WSNs) that improves routing scheme accuracy. The new algorithm, based on real-time evaluation using semi-supervised learning, enhances data transmission reliability.

Keywords:
evaluationmulti-path routingsemi-supervised learningwireless sensor network

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Single-path transmission in wireless sensor networks (WSNs) faces challenges with data reliability and network lifespan.
  • Existing multi-path routing algorithms often lack the accuracy needed for optimal performance.

Purpose of the Study:

  • To enhance the accuracy of multi-path routing schemes in WSNs.
  • To develop a novel multi-path routing algorithm leveraging semi-supervised learning for improved evaluation.

Main Methods:

  • Designed and implemented a real-time evaluation algorithm based on semi-supervised learning (RESL).
  • Developed a multi-path routing algorithm for WSNs incorporating RESL (MRSSL).
  • Conducted comparative experiments to validate the performance of RESL and MRSSL.

Main Results:

  • RESL demonstrated superior performance in both evaluation time and accuracy compared to existing methods.
  • MRSSL significantly improved the accuracy of the multi-path routing scheme in WSNs.
  • Experimental results confirmed the advantages of the proposed MRSSL algorithm.

Conclusions:

  • The proposed RESL algorithm offers efficient and accurate real-time evaluation for WSNs.
  • The MRSSL algorithm effectively enhances multi-path routing scheme accuracy, addressing limitations of current approaches.
  • This research contributes to more reliable and efficient data transmission in wireless sensor networks.